The role of anthropogenic vs. natural in-stream structures in determining connectivity and genetic diversity in an endangered freshwater fish, Macquarie perch (Macquaria australasica)

ORIGINAL ARTICLE

The role of anthropogenic vs. natural in-stream structuresin determining connectivity and genetic diversity in anendangered freshwater fish, Macquarie perch (Macquariaaustralasica)Leanne K. Faulks,1 Dean M. Gilligan2 and Luciano B. Beheregaray1,3

1 Department of Biological Sciences, Molecular Ecology Lab, Macquarie University, Sydney, NSW, Australia

2 Industry and Investment New South Wales, Batemans Bay Fisheries Office, Batemans Bay, NSW, Australia

3 School of Biological Sciences, Flinders University, Adelaide, SA, Australia

Introduction

At large spatial and temporal scales, such as across land-

scapes and geological time, habitat fragmentation is a

natural process that promotes the diversification of

ecological niches and the evolution of species. However,

contemporary habitat fragmentation is largely induced by

anthropogenic activities and usually occurs at a much fas-

ter rate than the natural process. Indeed, habitat fragmen-

tation is now recognized as one of the leading threats to

ecosystems worldwide (Kingsford et al. 2009). Freshwater

environments are naturally fragmented by mountain

ranges and coastlines that delineate basin boundaries and

are also structured as hierarchical riverine networks

(Fausch et al. 2002; Magalhaes et al. 2002; Boys and

Thoms 2006) with features such as waterfalls and cascades

acting as natural barriers. Some of the most disruptive

anthropogenic activities in freshwater environments are

the construction of impoundments and agricultural or

industrial practices (Cadwallader 1978; Allan and Flecker

1993; Maitland 1995). For example, the construction of

impoundments can affect habitat structure, change

macro-invertebrate community composition, alter natural

flow regimes and impede fish dispersal (Petts 1984;

Gehrke et al. 2002). Changes in water quality can also

result from impoundment construction but are often a

consequence of catchment land use such as clearing for

agriculture and mining activities (Dudgeon et al. 2006;

Growns et al. 2009). These landscape-level processes can

impact immediate in-stream habitats as well as have

cascading effects at larger scales within a river basin.

Similarly, habitat fragmentation can have both direct

and indirect consequences at the population level. Habitat

loss and fragmentation are known to result in reduced

Keywords

Australia, habitat fragmentation,

impoundment, landscape and conservation

genetics, phylogeography, riffle.

Correspondence

Luciano Beheregaray, School of Biological

Sciences, Flinders University, Adelaide 5001,

SA, Australia.

Tel.: +61 8 8201 5243; fax: +61 8 8201

3015; e-mail: luciano.beheregaray@flinders.

edu.au

Received: 14 December 2010

Accepted: 15 December 2010

doi:10.1111/j.1752-4571.2011.00183.x

Abstract

Habitat fragmentation is one of the leading causes of population declines,

threatening ecosystems worldwide. Freshwater taxa may be particularly sensitive

to habitat loss as connectivity between suitable patches of habitat is restricted

not only by the natural stream network but also by anthropogenic factors. Using

a landscape genetics approach, we assessed the impact of habitat availability on

population genetic diversity and connectivity of an endangered Australian fresh-

water fish Macquarie perch, Macquaria australasica (Percichthyidae). The rela-

tive contribution of anthropogenic versus natural in-stream habitat structures in

shaping genetic structure and diversity in M. australasica was quite striking.

Genetic diversity was significantly higher in locations with a higher river slope,

a correlate of the species preferred habitat – riffles. On the other hand, barriers

degrade preferred habitat and impede dispersal, contributing to the degree of

genetic differentiation among populations. Our results highlight the importance

of landscape genetics to understanding the environmental factors affecting fresh-

water fish populations and the potential practical application of this approach

to conservation management of other freshwater organisms.

Evolutionary Applications ISSN 1752-4571

ª 2011 Blackwell Publishing Ltd 1

population sizes and reduced demographic and genetic

connectivity (Ewers and Didham 2006; Lowe and Allen-

dorf 2010). The latter takes place because increased isola-

tion and novel ecological boundaries can impact on the

relative contribution of net immigration to demographic

connectivity – total population recruitment and also

genetic connectivity – the effective number of dispersers

(Lowe and Allendorf 2010). In turn, fragmented popula-

tions are supposed to experience reduced levels of genetic

diversity, population growth rate and population viability

(Frankham 1995; Ewers and Didham 2006) and increased

risk of local extinction (Spielman et al. 2004). Ongoing

habitat fragmentation also impacts negatively on evolu-

tionary resilience – the ability of populations to persist in

their current state and to undergo evolutionary adapta-

tion in response to a changing environment (sensu Blan-

chet et al. 2010; Sgro et al. 2010; Ewers and Didham

2006).

The eventual impact of these demographic and genetic

changes on a species can be influenced by the species

biology, particularly dispersal ability, life-history charac-

teristics and habitat requirements. Dispersal potential

enables species to respond to changes in the availability

of preferred habitat and can be crucial in determining the

effect of habitat fragmentation on genetic diversity (Raey-

makers et al. 2008; Watanabe et al. 2010). A recent review

revealed a significant negative association between the

genetic diversity of fish populations and human distur-

bance (DiBattista 2008), and there are several examples in

freshwater species (e.g. Belfiore and Anderson 2001). The

majority of studies of freshwater taxa have focused on the

impact of fragmentation on key migratory species such as

salmon; however, there is increasing recognition of whole

community effects (Gehrke et al. 2002). For species that

are naturally more sedentary, the genetic consequences of

habitat fragmentation such as reduced diversity could be

accentuated (Dehais et al. 2010; McCarney et al. 2010).

A valuable approach for exploring the influence of con-

temporary environmental factors on genetic diversity is

landscape genetics, an area of study that integrates popu-

lation genetics, environmental data and spatial statistics

(Manel et al. 2003; Storfer et al. 2007; Holderegger and

Wagner 2008). Landscape genetics can provide a greater

understanding of the interactions between species, ecosys-

tems and anthropogenic activities and can contribute to

conservation management of endangered species and the

environments in which they reside. Here, we use a land-

scape genetics approach to assess the impact of habitat

availability on population genetic diversity and structure

of an endangered (Australian Environment Protection and

Biodiversity Conservation Act 1999) Australian freshwater

fish, the Macquarie perch Macquaria australasica (Perc-

ichthyidae). This species is a habitat specialist and

remnant populations are found in rocky gorge country

with an abundance of riffles and where water quality is

good (Bruce et al., 2008; Gilligan et al. 2010). Adults are

thought to undergo small-scale migrations during the

breeding season to spawn in riffle beds (see Methods for

more details on species biology). These characteristics

make M. australasica an ideal species with which to test

the effect of habitat fragmentation on organisms with

limited dispersal behaviour and specific habitat require-

ments. Specifically, we evaluated the roles of natural habi-

tats (i.e. riffles) as well as anthropogenic in-stream

structures (i.e. weirs) in driving levels of genetic diversity,

population structure and contemporary patterns of

genetic connectivity. We predicted that the availability of

optimal habitat (riffles) would promote genetic diversity

and conversely that habitat fragmentation (anthropogenic

structures) would decrease genetic diversity and restrict

connectivity. We anticipate that the outcomes of this

study will contribute towards the conservation manage-

ment of this endangered species. In addition, the results

should further our understanding of the genetic conse-

quences of habitat loss and fragmentation on other fresh-

water organisms that have specific habitat requirements

and show limited dispersal behaviour.

Methods

Species distribution, status and biology

Remnant populations of M. australasica are restricted to

the headwaters of the Lachlan, Murrumbidgee, Murray,

Kiewa, Ovens, Goulburn-Broken and Campaspe Rivers in

the Murray-Darling Basin (MDB) to the west of the Great

Dividing Range (GDR), as well as the Hawkesbury-Nepean

(HN), Georges River and Shoalhaven basins on the east

of the GDR (Industry and Investment NSW Freshwater

Fish Research Database; Lintermans 2007) (Fig. 1). Previ-

ous genetic and morphological studies have provided

strong support for the differentiation of lineages on either

side of the GDR (Dufty 1986; Faulks et al. 2010a). During

the early 1900s, translocation occurred from the MDB

into the Yarra River (Victoria), Mongarlowe River (New

South Wales) and Cataract Dam (New South Wales)

(Cadwallader 1981). However, interbasin translocations

are no longer undertaken. Although both lineages co-

occur in the Cataract River (because of the translocation

of individuals), the extent of hybridization between them

is unknown and the taxonomic status of these two groups

is yet to be established (Faulks et al. 2010a). Despite the

species being protected throughout its natural range, the

natural population in the Shoalhaven catchment (Kanga-

roo River) declined rapidly during the late 1990s, and

despite intensive sampling effort (118 sampling occa-

sions), no individuals have been observed in the wild

Anthropogenic vs. natural in-stream structures in M. australasica Faulks et al.

2 ª 2011 Blackwell Publishing Ltd

since 1998. Monitoring of M. australasica populations

elsewhere indicates that the species is absent from up to

95% of its previous distribution (Bruce et al. 2008; Davies

et al. 2008). Loss of habitat and the construction of

impoundments are likely to be largely responsible for

these declines (Lintermans 2007; Bruce et al. 2008).

Other causes of population decline include increased

competition from native species following stocking, intro-

duced species (Perca fluviatilis and salmonids), overfishing

and susceptibility to epizootic haematopoietic necrosis

virus (EHNV) (Cadwallader 1978; McDowall, 1996; Allen

et al. 2002; Lintermans 2007).

Macquaria australasica adults from the MDB reach an

average length of 350 mm, and sexual maturity is reached

at 2 years for males and 3 years for females. Details of the

age, growth and maturity of coastal populations are still

unknown. Reproductive conditioning occurs during late

winter/spring, and adults undergo small-scale migrations

from pools to riffles to spawn in spring/early summer

when water temperature reaches approximately 15�C

(Wharton 1968; Cadwallader and Douglas 1986). Eggs

and larvae are demersal and remain in the riffles and the

extent of dispersal in juvenile stages is also thought to be

limited, resulting in localized recruitment (Cadwallader

and Rogan 1977). Even at this small scale, anthropogenic

structures have the potential to interfere with the species

reproductive cycle by restricting movement and habitat

availability.

Sample collection and DNA extraction

A total of 299 samples from 26 locations (n = 5–40) were

obtained from across the species range (Fig. 1) over sev-

eral years (2003–2008). Despite the potential for temporal

instability in population structure (e.g. McCairns and

Bernatchez, 2008), in this case, localized recruitment and

restricted dispersal mean that it is unlikely to have

influence on our results. Fish were collected using nonde-

structive methods such as electrofishing and bait trapping.

Small pieces of caudal fin tissue were taken, placed in

100% ethanol and stored at )20�C in the laboratory.

Total DNA was extracted from the tissue using a

Figure 1 Map of Australia indicating the study region. The first inset shows details of sampling locations of Macquaria australasica. The second

inset provides further details of sampling locations in the Hawkesbury-Nepean Basin. Dotted line indicates the Great Dividing Range (GDR).

Faulks et al. Anthropogenic vs. natural in-stream structures in M. australasica


salting-out method. We modified the method of Sun-

nucks and Hales (1996) by doubling the volume of TNES

(600 lL) in the digestion step and saturated salt solution

(170 lL) in the precipitation step. One sample from the

South Australian Museum and a few other samples that

yielded low-quality DNA using the salting-out method

were extracted using a QIAGEN DNeasy kit (Qiagen,

Hilden, Germany).

Microsatellite genotyping

Eight microsatellite DNA loci were amplified using PCR

primers originally developed for Australian Bass (Macqua-

ria novemaculeata) [AB009 (Schwartz et al. 2005)] and

Murray Cod (Macculochella peelii) [Mpe3.B11, Mpe2.E01,

Mpe1.F01, Mpe2.F07, Mpe3.G04, Mpe3.G12, Mpe1.H04

(Rourke et al. 2007)]. The forward primer for each primer

pair was incorporated with a fluorescent-labelled M13 tag

(Schuelke 2000). Loci were amplified and fluorescent dyes

incorporated in either single (AB009 VIC, Mpe3.B11

NED, Mpe2.E01 NED, Mpe3.G12 VIC) or multiplex

(Mpe3.G04 and Mpe1.H04 PET, Mpe1.F01 and Mpe2.F07

FAM) reactions. PCRs consisted of 1 lL template DNA,

2.5 mmol MgCl2, 60 mm KCl, 12 mm Tris–HCl pH 9.0,

0.12% Triton-X, 0.4 mm each dNTP, 0.78 pmol M13 for-

ward primer, 3.8 pmol reverse primer, 3.8 pmol M13 fluo-

rescent dye, 0.5 lg bovine serum albumin, 0.25 U Taq

DNA polymerase (Promega, Madison, WI, USA) and

dH2O to a final volume of 10 lL. PCR conditions were as

described in Schwartz et al. (2005). PCR products for the

eight loci (3–4 lL) for each sample were pooled, and 1 lL

of this product was mixed with 0.1 lL LIZ500 ladder and

9.9 lL HiDi. Products were then screened on an ABI

PRISM 377 with Genescan software (Applied Biosystems,

Carlsbad, CA, USA) at the Macquarie University DNA

Analysis Facility. Genotypes were visually inspected and

scored using GeneMapper (Applied Biosystems). In cases

of poor amplification, re-runs were conducted with a sin-

gle locus per reaction and without pooling. Genotypes

were checked for scoring errors because of null alleles,

stutter and large allele dropout using Microchecker 2.2.3

(van Oosterhout et al. 2004).

Genetic diversity

Fisher’s exact test of linkage disequilibrium between all

pairs of loci and conformance to Hardy–Weinberg

equilibrium in each population across all loci were tested

using Genepop 3.4 (Raymond and Rousset 1995).

Significance levels were Bonferroni-corrected to

address the risk of increased Type 1 error associated with

multiple tests (Rice 1989) and Benjamini-Yekutieli (B-Y)-

corrected to balance the risks of Type 1 and Type 2 errors

(Narum 2006). The expected (HE) and observed (HO) het-

erozygosities were calculated in Arlequin 3.1 (Excoffier

et al. 2006), and the corrected allelic richness (AR) and

inbreeding coefficient (FIS) per population were calculated

in FSTAT 2.9.3.2 (Goudet 2002). To evaluate whether pop-

ulations may have experienced recent bottlenecks, we cal-

culated the M ratio [Arlequin 3.1 (Garza and Williamson

2001)] and levels of heterozygosity [Bottleneck 1.2.02 (Piry

et al. 1999)]. The M ratio is the mean ratio of the number

of alleles compared to the range in allele size, and values

smaller than 0.68 indicate that the population has experi-

enced a recent and severe reduction in population size

(Garza and Williamson 2001). Hybridization can invalidate

the M ratio; therefore the Cataract River population, where

translocation of distinct lineages has occurred, was not

tested. Excess heterozygosity, owing to a faster rate of the

number of alleles lost compared to the reduction in gene

diversity, was assessed using both the stepwise (SMM) and

two-phased models of mutation (TPM) and a Wilcoxon

test in Bottleneck (Piry et al. 1999).

Population genetic structure

Population differentiation was evaluated using global and

pairwise FST tests, and levels of population subdivision

were assessed (AMOVA) among basins and among loca-

tions within basins. Weir and Cockerhams (Weir and

Cockerham 1984) FST values and significance levels were

calculated in Arlequin 3.1 (Excoffier et al. 2006). We used

FST rather than RST because of our relatively small number

of loci (<20) and recently diverged populations (Gaggiotti

et al. 1999), as shown in our mtDNA phylogeographical

study of this species (Faulks et al. 2010a).

Preliminary data analyses using both STRUCTURE

(nonspatial) (Pritchard et al. 2000) and Geneland (spa-

tial) (Guillot et al. 2005) to estimate population structure

obtained similar results. Geneland was chosen for final

analyses as the incorporation of spatial information pro-

vides a more realistic representation of the study system,

optimizing the ability to determine population structure

(sensu Dionne et al. 2008; Latch et al. 2008). All translo-

cated populations were removed from this analysis (Yarra

River, Mongarlowe River, Cataract Dam and River and

Loddon Ck). Geneland uses a Markov chain Monte Carlo

(MCMC) approach to identify genetic spatial discontinu-

ities. Five replicate runs were performed, each with

1 · 106 MCMC iterations, the Dirichlet model of allele

frequencies, no uncertainty in the spatial coordinates,

maximum rate of Poisson process 100 and the maximum

number of nuclei in the Poisson-Voronoi tessellation set

to 200. Plots of MCMC runs were assessed for mixing.

The most probable number of population clusters (K)

was the same for each run. The final output displays



maps of posterior probabilities of locations belonging to a

particular genetic cluster (K) or population. Although

these clusters (classes) may include areas of suboptimal

habitat, the important consideration is that a class con-

tains populations that are grouped together with a higher

probability than with populations from any other class.

Gene flow

Estimates of recent gene flow across the GDR as well as

among populations within the MDB and east coast were

assessed in BayesAss+ 1.3 (Wilson and Rannala 2003).

The program was run for 3 · 106 iterations including a

burn-in of 1 · 106 iterations. Delta values for allele fre-

quencies, inbreeding coefficients and migration rate were

set at 0.05, 0.10 and 0.02 for the entire data set; 0.45,

0.45 and 0.25 in the MDB; and 0.35, 0.35 and 0.15 on the

east coast. These values achieved the recommended accep-

tance rates of changes of 40–60% (Wilson and Rannala

2003). Convergence was assessed by plotting the cumula-

tive log likelihoods of the iterations. Estimates were con-

sidered ‘real’ if they were consistent in at least seven of 10

replicate runs, and the run with the best acceptance rates

and convergence was chosen for the parameter estimates.

Landscape genetics

We used a general linear model selection approach (e.g.

Banks et al. 2007) to identify the environmental variables

influencing genetic diversity [allelic richness (AR)]. AR was

used as the measure of diversity as it is a sensitive indicator

of changes in population demography (Nei et al. 1975).

The following environmental variables were considered:

distance of the site from the source (headwater), river

slope, total number of anthropogenic barriers within the

entire reach (including all upstream tributaries and down-

stream to the junction with the next larger waterway in the

stream hierarchy), riparian land use immediately surround-

ing the location (ranked from 1 to 4 in increasing order of

disturbance) and elevation and latitude of the sampling

location. Distance from the source was measured in Arc-

Map. River slope was calculated as the change in altitude in

m/km from 5 km upstream to 5 km downstream of each

sampling location. This measure was used as a surrogate of

potential riffle habitat within the reach. Although river

slope may also indicate flow velocity and valley shape, this

approach is considered appropriate in the absence of

detailed habitat mapping across the species distribution

(Jowett 1993; Thompson et al. 2006). The number of barri-

ers was calculated using the I&I NSW database (I&I NSW,

2008). These data include all structures of all ages, ranging

from culverts on road crossings to weirs and large dams

on impoundments (I&I NSW, 2008). Unfortunately,

equivalent data from catchments in Victoria were unavail-

able, so data from those locations were removed from this

analysis. Land use was inferred from Global Map 2001

(Geoscience Australia) and was categorized as follows:

1 = Forests, 2 = Grasslands, 3 = Water, 4 = Agriculture.

For the model selection analyses, all variables were stan-

dardized to a mean of zero and a standard deviation of

one. The assumptions of general linear models, including

normality and colinearity of variables, were assessed prior

to performing the modelling analyses. Only two of the vari-

ables were slightly skewed to lower values: river slope and

distance from the headwater source. In addition, only five

of the 15 pairwise comparisons were significantly corre-

lated, with none of the variables consistently correlated

with all others (Appendix 2). Therefore, all variables were

retained in the modelling analyses.

The best models were selected using Akaike’s Informa-

tion Criterion for small sample sizes (AICc) (Burnham

and Anderson 2002) and the independent effect of each

variable to the model estimated using hierarchical parti-

tioning (Walsh and MacNally 2008). In addition, we con-

ducted a re-sampling-based regression analysis to estimate

the effect of our environmental variables on AR while

controlling for unequal sample sizes among sites. In each

of 10 000 iterations, we re-sampled an equal number of

individuals (5) from each population, recalculated AR

over loci within a population and fitted a simple linear

regression model of the best environmental variable to

the AR data. We obtained the distribution (95% CI) of

the regression coefficient and considered our conclusions

robust if >97.5% of the coefficients were either consis-

tently positive or consistently negative.

Finally, we performed simple and partial Mantel tests

in FSTAT 2.9.3.2 (Goudet 2002) to determine the effect

of anthropogenic barriers and riverine distance on popu-

lation differentiation (FST). These tests were conducted

on the coastal populations only, because of the small

number of sites for which data were available in the

MDB. Riverine distances between pairs of populations

were calculated using ArcMap, and the number of barri-

ers between populations was calculated using the I&I

NSW database (I&I NSW, 2008). To investigate the effect

of variable samples sizes, we performed two tests, the first

with FST values calculated from the entire data set and

the second with FST values calculated from a single ran-

dom selection of five individuals from each population.

Each test involved 2000 permutations.

Results

Genetic diversity

We amplified eight polymorphic loci with no consistent

evidence for stutter, large allele dropout or linkage



disequilibrium. Some loci showed evidence of null alleles

(E01, G04 and G12). However, these were not consistent

across populations, and when analyses were run without

E01, G04 and G12, similar results were obtained. Thirteen

populations (of 26) deviated significantly from Hardy–

Weinberg equilibrium when averaged over all loci (15 of

26 using the B-Y correction) (Table 1). These deviations

are most likely due to local inbreeding effects as shown

by the high inbreeding coefficients (FIS > 0, Table 1) and

the lack of loci specific patterns of disequilibrium. Diver-

sity indices for each population are also shown in

Table 1. Overall, the species showed very low genetic

diversity. Mean allelic richness was only 1.4, and mean

observed heterozygosity was 0.28. The M ratio results for

TPM and SMM were the same and indicated that most

populations have undergone recent bottlenecks. However,

results from the Wilcoxon test of excess heterozygosity

were more conservative and only eight populations

showed evidence of bottlenecks (Table 1): Broken, Buf-

falo, Cotter, Bowen, Cataract Dam, Cordeaux, Loddon

and Wheeny. Four of these populations are in or above

major impoundments.

Population structure

There was a high degree of population structure across

the species range (FST = 0.43, P < 0.001). Pairwise fixa-

tion indices (Appendix 1) had a large range (FST = 0.00–

0.68) and were highest between the Webbs and Wongaw-

illi populations on the coast. Geneland separated samples

Table 1. Genetic diversity indices characterized at eight microsatellite loci for Macquaria australasica populations.

Location N Mean AR Mean HO Mean HE FIS P value M ratio

MDB

AB 40 1.5 0.41 0.50 0.19 <0.001* 0.56

BR 5 1.45 0.28 0.45 0.42 0.12 0.54*

BUFF 6 1.3 0.30 0.30 0.00 0.69 0.62*

CO 10 1.28 0.12 0.28 0.60 <0.001* 0.60*

DD 7 1.46 0.33 0.46 0.30 0.14 0.52

HU 9 1.42 0.33 0.42 0.22 0.10 0.60

KPC 9 1.44 0.23 0.44 0.50 <0.001* 0.49

LN 14 1.54 0.42 0.54 0.23 <0.001* 0.39

MB 20 1.37 0.15 0.37 0.59 <0.001* 0.40

YR 12 1.5 0.33 0.50 0.36 <0.001* 0.40

COAST

BOC 6 1.37 0.25 0.37 0.34 0.07 0.60*

CD 6 1.41 0.29 0.41 0.31 0.41 0.53*

CR 16 1.59 0.33 0.59 0.44 <0.001* NA

COR 6 1.4 0.22 0.40 0.47 0.03 0.65*

CX 12 1.57 0.40 0.57 0.32 <0.001* 0.44

EC 7 1.54 0.43 0.54 0.25 0.11 0.42

GB 12 1.35 0.18 0.35 0.50 <0.001* 0.51

KD 12 1.59 0.41 0.59 0.31 <0.001* 0.45

KW 24 1.63 0.42 0.63 0.34 <0.001* 0.46

LD 8 1.38 0.18 0.38 0.55 0.01* 0.50*

LT 9 1.57 0.42 0.57 0.30 0.003* 0.34

MON 6 1.34 0.21 0.34 0.42 0.02 0.53

WB 5 1.21 0.05 0.21 0.79 0.07 0.83

WC 12 1.35 0.09 0.35 0.75 <0.001* 0.51*

WL 18 1.39 0.30 0.39 0.22 <0.001* 0.45

WOC 8 1.26 0.14 0.26 0.46 0.02 0.69

N is the sample size, AR is allelic richness standardized for sample size, HO and HE are the observed and expected heterozygotes, FIS is the inbreed-

ing coefficient (FIS > 0 evidence of inbreeding), the P value relates to test of Hardy–Weinberg equilibrium with significant values after Bonferroni

correction indicated in bold (P < 0.0019) and after B-Y correction indicated with an asterisk (P < 0.013), M ratios indicating bottlenecks (Arlequin;

M < 0.68) are highlighted in bold, and asterisks indicate significant excess heterozygosity and/or shifted mode as calculated in BOTTLENECK. NA

indicates the test was not performed (see Methods).

AB, Abercrombie R; BR, Broken R; BUFF, Buffalo Ck; CO, Cotter Dam; DD, Dartmouth Dam; HU, Hughes Ck; KPC, King Parrot Ck; LN, Lachlan R;

MB, Murrumbidgee R; YR, Yarra R; BOC, Bowen Ck; CD, Cataract Dam; CR, Cataract R; COR, Cordeaux Dam; CX, Coxs R; EC, Erskine Ck;

GB, Glenbrook Ck; KD, Kedumba R; KW, Kowmung R; LD, Loddon Ck; LT, Little R; MON, Mongarlowe R; WB, Webbs Ck; WC, Wheeny Ck; WL,

Wollemi Ck; WOC, Wongawilli Ck.



into four major groups, which correspond broadly with

catchment structure: Class 1. upper-mid-HN (Kowmung,

Coxs, Kedumba, Little, Erskine, Glenbrook), Class 2.

lower HN (Bowens, Cordeaux, Wollemi, Wongawilli,

Webbs), Class 3. Murrumbidgee and Class 4. Lachlan and

Murray. The inclusion of Cordeaux Dam and Wongawilli

with the lower HN group (Class 2) may indicate that

some long-distance dispersals or localized translocations

have occurred in the HN. However, there were no reliable

estimates of migration among the coastal populations as

BayesAss+ results were inconsistent between runs (<7 of

10 runs). There was very little recent gene flow across the

GDR, with the proportion of migrant individuals per gen-

eration being 0.004 from the HN to the MDB (CI 0.000–

0.016) and 0.010 from the MDB to the HN (CI 0.000–

0.036). In contrast, estimates of recent gene flow among

populations of the MDB consistently (10 of 10 runs)

indicated significant amounts of migration from the

Abercrombie to the Lachlan [0.231 (CI 0.121–0.314)].

However, estimates of migration among the remaining

MDB populations were inconsistent between runs (<7 of

10 runs).

Landscape genetics

The model that best explained levels of genetic diversity

within populations included just one environmental vari-

able: river slope (Table 2). Other highly ranked models

retained river slope but also included distance from the

headwater source (DAICc = 0.2892) and land use

(DAICc = 0.8075). Hierarchical partitioning of the data

indicated that the majority of variation in genetic diver-

sity among populations could be explained by variation

in the river slope (58.9%) and distance from the source

(21.5%). However, the only significant variable in all of

the models was river slope, which was positively corre-

lated with genetic diversity (AR) (Fig. 2). The results of

our regression analyses were shown to be robust to varia-

tion in sample size. Our re-sampling procedure indicated

that the coefficient for river slope was always positive

[0.041 CI (0.029, 0.052)]. Mantel tests revealed that both

riverine distance and the number of barriers between sites

are correlated with genetic differentiation (FST) among

the coastal populations (Appendix 3). The number of

barriers that separate localities explains a significant por-

tion of genetic divergence across localities (R2 = 0.25,

P = 0.0245), even when riverine distance is factored out

in partial Mantel correlation analysis. The same results

were obtained when we recalculated FST using a random

sample of five individuals from each population.

Discussion

Our study of an endangered freshwater fish shows that

recent habitat fragmentation leads to negative genetic

consequences. Genetic diversity in M. australasica was sig-

nificantly higher in locations with a higher river slope, a

surrogate for riffles, the species preferred habitat

structure. In contrast, barriers both fragment and degrade

preferred habitat and impede dispersal, contributing to

the genetic differentiation of populations. We highlight

the importance of the landscape genetics approach to

understand environmental factors affecting freshwater fish

populations and its potential practical application to

conservation management of other freshwater organisms

with specific habitat requirements and limited dispersal

ability.

Preferred habitat promotes genetic diversity

Macquaria australasica is known to be a habitat specialist,

as extensive habitat mapping and surveying within some

of the most abundant remnant populations (the upper

Lachlan and Abercrombie Rivers and the HN) suggest

Table 2. Models of environmental variables to explain the distribution of genetic variation at eight microsatellite loci (AR) among populations of

Macquaria australasica.

Model

rank

River

slope

km from

source Barriers

Land

use Elevation Latitude Intercept AICc deltaAICc

Rsqu

(adj) ANOVA

Sig

variables

1 0.0592 1.43 )29.3577 0 0.228 0.019 rs

2 0.0551 0.0357 1.43 )29.0685 0.2892 0.278 0.024 rs

3 0.0657 0.0436 1.44 )28.5502 0.8075 0.26 0.03 rs

Null (12) )25.5947 3.7631

Full (63) 0.0679 0.0359 )0.0014 0.0343 )0.0206 )0.0456 1.46 )15.2042 14.1535 0.145 0.242 rs

HP%IC 58.9 21.5 4.8 8.5 2.6 3.7

All variables are standardized to mean of zero and standard deviation of one. HP %IC, independent contribution of each environmental variable

to explaining the variation in AR; AICc, Akaike’s Information Criterion adjusted for small sample sizes; R2 (adj), R2 adjusted for the number of

variables in the multiple regression analysis; ANOVA, P value for the analysis of variation of environmental variables in the multiple regression

(significance at P < 0.01 following Bonferroni correction); Sig., variables making a significant contribution to the model according to the ANOVA;

rs, river slope.



that the area of riffles within a reach is one of the best

predictors of the species presence (Bruce et al., 2008;

Gilligan et al. 2010). Species-habitat association modelling

indicates that at least one ha and an optimum of three ha

of riffle habitat per kilometre of stream are required for

M. australasica to be present (Gilligan et al. 2010). Riffles

are known to be a vital factor for successful recruitment

of M. australasica populations (Cadwallader and Rogan

1977). These areas of the stream habitat are where demer-

sal eggs are laid and remain until hatching and where lar-

vae shelter during development. Therefore, a greater area

of riffles provides a greater area of suitable spawning sub-

stratum and may also improve the survival of larval fish.

Gilligan et al. (2010) indicated that the Abercrombie

River had more optimal habitat for the species, with 56%

of reaches containing suitable habitats, including riffles,

compared with 20% in the Lachlan River. Our measure

of riffle habitat, river slope, was also higher in the Aber-

crombie than in the Lachlan River (6.4 and 4.3 m/km,

respectively), supporting our use of this measure as a

surrogate for the species preferred habitat.

Although natural river structures and river slope have

been observed to influence the levels of genetic structure

in other species (Wofford et al. 2005; Boizard et al. 2009;

Blanchet et al. 2010; Cook et al. 2010), we believe there is

also a clear need to consider the patterns and processes

that influence genetic diversity of populations. This is

especially true in endangered species where management

resources need to be directed to populations of high

priority, such as those with reduced levels of genetic

diversity (Amos and Balmford 2001). Maintaining the

resilience and evolutionary potential of such populations

could be facilitated by enhancing the environmental

processes identified by landscape genetics studies to be

important drivers of genetic diversity.

Anthropogenic structures contribute to population

genetic structure and restricted gene flow

Overall, broad-scale population genetic structure reflected

drainage basin divisions with the clear differentiation of

the MDB and HN, as well as the major catchments within

these drainage basins. There was no evidence of signifi-

cant amounts of recent gene flow across the GDR. This

provides further support to our finding of historically iso-

lated lineages based on mtDNA data (Faulks et al. 2010a)

as well as studies that showed allozyme and morphologi-

cal differentiation among populations across the GDR

(Dufty 1986). In addition, many characteristics of the

species biology and ecology are thought to differ on either

side of the GDR (A. Bruce, personal communication).

Therefore, support for the designation of the coastal and

MDB forms as separate evolutionary significant units

(ESUs) (sensu Crandall et al., 2000) is strong, and further

investigation into the taxonomic distinction of these

forms should be considered a priority.

Although the movement patterns of M. australasica are

not well documented, the species has been observed

undertaking small migrations within a localized home

range (Katie Ryan, University of Canberra, personal com-

munication). Despite relatively limited dispersal behav-

iour, we have demonstrated that habitat fragmentation, in

the form of anthropogenic structures, has led to increased

genetic differentiation of populations. We hypothesize

that M. australasica may undertake rare long-distance

dispersal events that are hindered by anthropogenic

structures. Alternatively, habitat fragmentation may have

a cumulative negative effect on the species genetic archi-

tecture. Until further studies clarify patterns of dispersal

behaviour in M. australasica, the specific effect that

anthropogenic structures exert on the species will remain

unclear.

The separation of populations within the MDB is likely

driven by a combination of extensive geographical

distances, unsuitable habitat conditions as well as the

presence of major anthropogenic structures, e.g. Cotter

Dam, Dartmouth Dam and Wyangala Dam. The only evi-

dence of recent migration was from the Abercrombie

River into the Lachlan River, adjacent waterways in the

same catchment. Migration between the two populations

appears unidirectional with the Abercrombie potentially

acting as a source population for the Lachlan. It is possi-

ble that greater habitat quality and quantity within the

Abercrombie River (Gilligan et al. 2010) result in a larger

population size and consequent Ne within the Abercrom-

bie River population. Within the HN basin, the two pop-

ulation groups (upper-mid-HN and lower HN) are

separated not only by geographical distance, in some

cases the saline tidal waters in the Hawkesbury River estu-

ary, but also by anthropogenic barriers. For example, the

upper-mid-HN group contains populations above War-

ragamba and Cordeaux Dams; these are large impound-

ments that likely act as effective barriers to dispersal in

both downstream and upstream directions.

y = 0.0057x + 1.3921R2 = 0.228

11.11.21.31.41.51.61.71.81.9

2

0 5 10 15 20 25

AR

River slope (m/km)

Figure 2 Relationship between genetic diversity (AR) and river slope

(m/km).



Management

Macquaria australasica has experienced widespread popu-

lation declines (Bruce et al., 2008; Davies et al. 2008), and

despite extensive sampling effort, many of the populations

in this study are represented by relatively small sample

sizes. We acknowledge that sample sizes may have con-

tributed to the Hardy–Weinberg disequilibrium observed

in some populations as well as reduced the power of

BayesAss+ to detect recent migration. Nonetheless, simu-

lation studies with BayesAss+ have shown that increasing

sample size has only a moderate effect on the accuracy of

estimates, particularly when the number of loci and

migration rates are low (Wilson and Rannala 2003).

Importantly, the major findings of our study – the influ-

ence of habitat fragmentation (anthropogenic and natural

in-stream structures) on genetic structure and diversity –

are supported by re-sampling procedures designed to

account for variable and small sample sizes. Overall, we

believe that our results have significant biological mean-

ing and can contribute to the conservation management

of this species.

Levels of genetic variation in M. australasica were very

low, both in absolute values and in comparison with the

congeneric M. ambigua (Faulks et al. 2010b). Our analy-

ses also provided strong evidence for population bottle-

necks and inbreeding in M. australasica. The tests

performed are sensitive to the detection of inbreeding

within the past three generations (Piry et al. 1999; Goudet

2002), a period of at least 10 years for M. australasica.

The widespread construction of barriers to movement as

well as declines in population abundances across the spe-

cies range has occurred within this timeframe (Cadwall-

ader and Rogan 1977) and has most likely contributed to

the genetic deterioration of populations. We suggest that

in order to help boost genetic variation, there is a need to

mitigate the effects of habitat fragmentation and barrier

construction (Hughes 2007). The installation of fishways

to assist dispersal of fish over dams and weirs has proven

beneficial for rehabilitating fish communities in Australia

and overseas (Calles and Greenberg 2007; Stuart et al.

2008) and should be considered as an important compo-

nent in the management of M. australasica. Allowing

passage over barriers could provide M. australasica

populations with access to additional spawning habitat

and enhance recruitment success. The rehabilitation of

preferred habitat, e.g. removing sand or sedimentation

from riffles or enhancing environmental flows to scour

sediment loads, could also help increase population sizes.

Other management options being considered in the cur-

rent species recovery planning process include reducing

illegal fishing, reducing disease risk, establishing new

populations, increasing community awareness and

establishing a long-term monitoring programme (I&I

NSW, 2010). In addition, we recommend that the follow-

ing distinct catchment-scale groups be recognized to help

maintain the genetic diversity, the evolutionary resilience

and the evolutionary potential of the species as a whole:

Lachlan, Murrumbidgee, Murray, upper HN and lower

HN. In conclusion, we highlight the value of incorporat-

ing landscape genetics studies into established frameworks

such as the ESU, particularly in cases where conservation

outcomes can be achieved and assessed through adaptive

management strategies.

Acknowledgements

Samples were provided by Industry & Investment NSW,

Arthur Rylah Institute Department of Sustainability and

Environment Victoria, the South Australian Museum and

Peter Unmack. Meaghan Rourke provided advice on

marker selection. Sam Banks provided assistance with the

re-sampling analysis. Funding for this project was pro-

vided by the Australian Research Council (grant LP

0667952 to L. Beheregaray and D. Gilligan) and the New

South Wales Recreational Freshwater Fishing Trust

Expenditure Committee.

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80.4

90.3

80.4

40.0

0

HU

0.1

60.1

20.0

80.1

20.3

50.0

0

KPC

0.0

90.1

60.1

80.1

30.3

70.1

30.0

0

LN0.0

60.0

50.0

70.0

90.3

50.1

20.1

20.0

0

MB

0.1

90.2

90.2

50.3

00.3

00.2

20.2

10.2

60.0

0

YR

0.0

90.1

60.1

10.1

50.3

30.1

40.1

50.1

30.2

20.0

0

CD

0.1

20.1

80.1

30.1

50.3

40.1

60.0

90.1

20.2

20.1

50.0

0

LD0.2

40.3

10.2

30.3

20.3

70.2

70.2

40.1

40.3

20.3

10.1

60.0

0

MO

N0.1

90.2

20.2

20.2

50.3

60.1

90.1

20.2

10.0

20.1

60.1

60.2

90.0

0

BO

C0.4

40.5

80.4

70.5

30.6

10.4

70.5

20.4

20.5

30.4

60.5

30.5

30.5

50.0

0

CO

R0.4

30.5

40.4

40.5

00.5

80.4

40.4

90.4

00.5

20.4

40.5

00.5

00.5

30.2

80.0

0

CR

0.1

60.1

40.1

00.1

70.2

80.1

50.1

50.0

90.1

80.1

40.0

90.1

30.1

70.3

50.3

30.0

0

CX

0.3

40.4

50.3

70.4

00.4

80.3

80.4

00.3

30.4

40.3

40.4

00.4

40.4

40.3

20.2

70.2

30.0

0

EC0.3

80.4

80.4

00.4

00.5

10.4

00.4

30.3

70.5

00.3

20.4

10.4

80.4

70.4

30.4

30.2

80.1

50.0

0

GB

0.4

70.6

00.5

50.5

50.6

20.5

30.5

50.4

80.6

00.4

90.5

50.5

80.5

90.5

10.5

70.3

90.3

20.1

70.0

0

KD

0.3

60.3

90.3

50.3

40.4

60.3

60.3

70.3

20.4

30.3

40.3

80.4

10.4

10.2

90.2

50.2

60.0

50.1

70.2

90.0

0

KW

0.3

30.3

70.3

20.3

30.4

00.3

30.3

40.3

00.3

60.3

00.3

50.3

80.3

70.2

70.2

30.2

20.0

00.1

40.2

70.0

30.0

0

LT0.3

50.5

20.4

40.4

10.5

60.4

40.4

20.3

50.5

20.4

00.4

60.4

70.4

90.4

40.4

00.2

60.1

60.2

50.4

10.0

90.1

00.0

0

WB

0.5

00.6

70.6

00.6

00.7

10.5

90.5

90.5

00.6

60.5

60.6

20.6

20.6

50.4

60.5

20.4

40.4

00.4

40.5

50.3

40.3

20.4

20.0

0

WC

0.4

40.5

30.4

80.5

10.6

10.5

00.5

10.4

20.5

50.4

80.5

30.5

10.5

40.5

40.5

40.3

70.4

60.5

10.5

80.4

20.3

80.5

20.6

40.0

0

WL

0.4

60.5

70.5

30.5

20.6

10.5

10.5

20.4

60.5

70.4

90.5

50.5

50.5

70.4

50.5

40.4

00.3

90.3

40.3

40.3

50.3

50.3

60.4

60.5

50.0

0

WO

C0.4

80.6

50.5

30.6

00.6

50.5

10.5

70.4

50.6

00.5

30.5

90.5

40.6

30.5

50.3

40.4

00.3

70.5

20.6

40.3

60.3

00.4

90.6

80.6

30.6

00.0

0



Appendix 2

Matrix of the Pearson’s correlation coefficients for environmental vari-

ables used in the modelling procedure. Upper line = correlation coeffi-

cient, lower line = P value. Significant correlations are indicated by

italics. rs = river slope (m/km).

km Barriers rs Latitude Landuse

Barriers 0.496

0.026

rs 0.121 0.288

0.611 0.218

Latitude )0.129 0.052 0.483

0.588 0.829 0.031

Land use 0.191 0.169 )0.211 )0.084

0.419 0.476 0.372 0.725

Elevation 0.466 0.571 )0.193 )0.649 0.063

0.038 0.009 0.415 0.002 0.793

Appendix 3

Results of simple and partial Mantel tests to determine the relation-

ship between riverine distances, number of anthropogenic barriers

and genetic differentiation (FST) in coastal populations of Macquaria

australasica.

Variable P value Coefficient R2

Riverine km 0.0005 0.429 0.184

Barriers 0.0005 0.492 0.243

Barriers (km controlled) 0.0245 0.429 0.250

km (barriers controlled) 0.0005 0.257 0.250



The role of anthropogenic vs. natural in-stream structures in determining connectivity and genetic diversity in an endangered freshwater fish, Macquarie perch (Macquaria australasica)

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